Overview

Dataset statistics

Number of variables23
Number of observations1460
Missing cells119
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory262.5 KiB
Average record size in memory184.1 B

Variable types

Categorical13
Numeric9
Boolean1

Alerts

OverallQual is highly overall correlated with YearBuilt and 5 other fieldsHigh correlation
YearBuilt is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
YearRemodAdd is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
TotalBsmtSF is highly overall correlated with SalePriceHigh correlation
GrLivArea is highly overall correlated with OverallQual and 3 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with GrLivArea and 1 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with GrLivArea and 2 other fieldsHigh correlation
SalePrice is highly overall correlated with OverallQual and 5 other fieldsHigh correlation
ExterQual is highly overall correlated with OverallQualHigh correlation
Foundation is highly overall correlated with YearBuiltHigh correlation
BsmtQual is highly overall correlated with OverallQual and 1 other fieldsHigh correlation
MSZoning is highly imbalanced (56.9%)Imbalance
CentralAir is highly imbalanced (65.3%)Imbalance
Electrical is highly imbalanced (78.2%)Imbalance
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
GarageCond is highly imbalanced (87.6%)Imbalance
SaleType is highly imbalanced (75.3%)Imbalance
SaleCondition is highly imbalanced (62.5%)Imbalance
BsmtQual has 37 (2.5%) missing valuesMissing
GarageCond has 81 (5.5%) missing valuesMissing
TotalBsmtSF has 37 (2.5%) zerosZeros

Reproduction

Analysis started2023-10-11 18:47:22.664079
Analysis finished2023-10-11 18:47:27.268861
Duration4.6 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

MSZoning
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0342466
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1151
78.8%
RM 218
 
14.9%
FV 65
 
4.5%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2023-10-12T00:17:27.311435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:27.361681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
rl 1151
78.3%
rm 218
 
14.8%
fv 65
 
4.4%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2910
98.0%
Lowercase Letter 30
 
1.0%
Space Separator 10
 
0.3%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1385
47.6%
L 1151
39.6%
M 218
 
7.5%
F 65
 
2.2%
V 65
 
2.2%
H 16
 
0.5%
C 10
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 20
66.7%
a 10
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2940
99.0%
Common 30
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1385
47.1%
L 1151
39.1%
M 218
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
a 10
 
0.3%
Common
ValueCountFrequency (%)
10
33.3%
( 10
33.3%
) 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:27.418319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2023-10-12T00:17:27.476160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

HouseStyle
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9109589
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 726
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.5%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2023-10-12T00:17:27.533669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:27.606945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1story 726
49.7%
2story 445
30.5%
1.5fin 154
 
10.5%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5336
61.8%
Uppercase Letter 1562
 
18.1%
Decimal Number 1545
 
17.9%
Other Punctuation 187
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1208
22.6%
r 1208
22.6%
y 1208
22.6%
t 1171
21.9%
n 187
 
3.5%
i 162
 
3.0%
v 65
 
1.2%
l 65
 
1.2%
e 37
 
0.7%
f 25
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1273
81.5%
F 199
 
12.7%
L 65
 
4.2%
U 25
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 894
57.9%
2 464
30.0%
5 187
 
12.1%
Other Punctuation
ValueCountFrequency (%)
. 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6898
79.9%
Common 1732
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1273
18.5%
o 1208
17.5%
r 1208
17.5%
y 1208
17.5%
t 1171
17.0%
F 199
 
2.9%
n 187
 
2.7%
i 162
 
2.3%
L 65
 
0.9%
v 65
 
0.9%
Other values (4) 152
 
2.2%
Common
ValueCountFrequency (%)
1 894
51.6%
2 464
26.8%
5 187
 
10.8%
. 187
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

OverallQual
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:27.703954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2023-10-12T00:17:27.749767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

YearBuilt
Real number (ℝ)

HIGH CORRELATION 

Distinct112
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.2678
Minimum1872
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:27.799428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1872
5-th percentile1916
Q11954
median1973
Q32000
95-th percentile2007
Maximum2010
Range138
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.202904
Coefficient of variation (CV)0.015321563
Kurtosis-0.43955194
Mean1971.2678
Median Absolute Deviation (MAD)25
Skewness-0.61346117
Sum2878051
Variance912.21541
MonotonicityNot monotonic
2023-10-12T00:17:27.860121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 67
 
4.6%
2005 64
 
4.4%
2004 54
 
3.7%
2007 49
 
3.4%
2003 45
 
3.1%
1976 33
 
2.3%
1977 32
 
2.2%
1920 30
 
2.1%
1959 26
 
1.8%
1998 25
 
1.7%
Other values (102) 1035
70.9%
ValueCountFrequency (%)
1872 1
 
0.1%
1875 1
 
0.1%
1880 4
 
0.3%
1882 1
 
0.1%
1885 2
 
0.1%
1890 2
 
0.1%
1892 2
 
0.1%
1893 1
 
0.1%
1898 1
 
0.1%
1900 10
0.7%
ValueCountFrequency (%)
2010 1
 
0.1%
2009 18
 
1.2%
2008 23
 
1.6%
2007 49
3.4%
2006 67
4.6%
2005 64
4.4%
2004 54
3.7%
2003 45
3.1%
2002 23
 
1.6%
2001 20
 
1.4%

YearRemodAdd
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1984.8658
Minimum1950
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:27.918875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1950
5-th percentile1950
Q11967
median1994
Q32004
95-th percentile2007
Maximum2010
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.645407
Coefficient of variation (CV)0.010401412
Kurtosis-1.2722452
Mean1984.8658
Median Absolute Deviation (MAD)13
Skewness-0.503562
Sum2897904
Variance426.23282
MonotonicityNot monotonic
2023-10-12T00:17:27.977468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 178
 
12.2%
2006 97
 
6.6%
2007 76
 
5.2%
2005 73
 
5.0%
2004 62
 
4.2%
2000 55
 
3.8%
2003 51
 
3.5%
2002 48
 
3.3%
2008 40
 
2.7%
1996 36
 
2.5%
Other values (51) 744
51.0%
ValueCountFrequency (%)
1950 178
12.2%
1951 4
 
0.3%
1952 5
 
0.3%
1953 10
 
0.7%
1954 14
 
1.0%
1955 9
 
0.6%
1956 10
 
0.7%
1957 9
 
0.6%
1958 15
 
1.0%
1959 18
 
1.2%
ValueCountFrequency (%)
2010 6
 
0.4%
2009 23
 
1.6%
2008 40
2.7%
2007 76
5.2%
2006 97
6.6%
2005 73
5.0%
2004 62
4.2%
2003 51
3.5%
2002 48
3.3%
2001 21
 
1.4%

ExterQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.1%
Gd 488
33.4%
Ex 52
 
3.6%
Fa 14
 
1.0%

Length

2023-10-12T00:17:28.032898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:28.076092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.1%
gd 488
33.4%
ex 52
 
3.6%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2366
81.0%
Lowercase Letter 554
 
19.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 906
38.3%
A 906
38.3%
G 488
20.6%
E 52
 
2.2%
F 14
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
d 488
88.1%
x 52
 
9.4%
a 14
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Foundation
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.5157534
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 647
44.3%
CBlock 634
43.4%
BrkTil 146
 
10.0%
Slab 24
 
1.6%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2023-10-12T00:17:28.127548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:28.176937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pconc 647
44.3%
cblock 634
43.4%
brktil 146
 
10.0%
slab 24
 
1.6%
stone 6
 
0.4%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5166
64.2%
Uppercase Letter 2887
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1293
25.0%
c 1281
24.8%
l 804
15.6%
k 780
15.1%
n 653
12.6%
i 146
 
2.8%
r 146
 
2.8%
a 24
 
0.5%
b 24
 
0.5%
t 6
 
0.1%
Other values (2) 9
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1281
44.4%
B 780
27.0%
P 647
22.4%
T 146
 
5.1%
S 30
 
1.0%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8053
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtQual
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)0.3%
Missing37
Missing (%)2.5%
Memory size11.5 KiB
TA
649 
Gd
618 
Ex
121 
Fa
 
35

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2846
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 649
44.5%
Gd 618
42.3%
Ex 121
 
8.3%
Fa 35
 
2.4%
(Missing) 37
 
2.5%

Length

2023-10-12T00:17:28.229109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:28.273028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 649
45.6%
gd 618
43.4%
ex 121
 
8.5%
fa 35
 
2.5%

Most occurring characters

ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2072
72.8%
Lowercase Letter 774
 
27.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 649
31.3%
A 649
31.3%
G 618
29.8%
E 121
 
5.8%
F 35
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
d 618
79.8%
x 121
 
15.6%
a 35
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 649
22.8%
A 649
22.8%
G 618
21.7%
d 618
21.7%
E 121
 
4.3%
x 121
 
4.3%
F 35
 
1.2%
a 35
 
1.2%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:28.324354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2023-10-12T00:17:28.381477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

CentralAir
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True 1365
93.5%
False 95
 
6.5%
2023-10-12T00:17:28.429205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Electrical
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size11.5 KiB
SBrkr
1334 
FuseA
 
94
FuseF
 
27
FuseP
 
3
Mix
 
1

Length

Max length5
Median length5
Mean length4.9986292
Min length3

Characters and Unicode

Total characters7293
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1334
91.4%
FuseA 94
 
6.4%
FuseF 27
 
1.8%
FuseP 3
 
0.2%
Mix 1
 
0.1%
(Missing) 1
 
0.1%

Length

2023-10-12T00:17:28.481571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:28.533482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1334
91.4%
fusea 94
 
6.4%
fusef 27
 
1.9%
fusep 3
 
0.2%
mix 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4376
60.0%
Uppercase Letter 2917
40.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 2668
61.0%
k 1334
30.5%
u 124
 
2.8%
s 124
 
2.8%
e 124
 
2.8%
i 1
 
< 0.1%
x 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
S 1334
45.7%
B 1334
45.7%
F 151
 
5.2%
A 94
 
3.2%
P 3
 
0.1%
M 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 7293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 2668
36.6%
S 1334
18.3%
B 1334
18.3%
k 1334
18.3%
F 151
 
2.1%
u 124
 
1.7%
s 124
 
1.7%
e 124
 
1.7%
A 94
 
1.3%
P 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:28.587820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2023-10-12T00:17:28.769191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2023-10-12T00:17:28.821646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:28.865808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:28.907489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2023-10-12T00:17:28.953831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2023-10-12T00:17:29.003867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.047220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

TotRmsAbvGrd
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:29.087543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2023-10-12T00:17:29.133006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2023-10-12T00:17:29.181765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.226295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2023-10-12T00:17:29.273363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.318872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageCond
Categorical

IMBALANCE  MISSING 

Distinct5
Distinct (%)0.4%
Missing81
Missing (%)5.5%
Memory size11.5 KiB
TA
1326 
Fa
 
35
Gd
 
9
Po
 
7
Ex
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2758
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1326
90.8%
Fa 35
 
2.4%
Gd 9
 
0.6%
Po 7
 
0.5%
Ex 2
 
0.1%
(Missing) 81
 
5.5%

Length

2023-10-12T00:17:29.368363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.411742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ta 1326
96.2%
fa 35
 
2.5%
gd 9
 
0.7%
po 7
 
0.5%
ex 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2705
98.1%
Lowercase Letter 53
 
1.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1326
49.0%
A 1326
49.0%
F 35
 
1.3%
G 9
 
0.3%
P 7
 
0.3%
E 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
a 35
66.0%
d 9
 
17.0%
o 7
 
13.2%
x 2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 2758
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2758
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1326
48.1%
A 1326
48.1%
F 35
 
1.3%
a 35
 
1.3%
G 9
 
0.3%
d 9
 
0.3%
P 7
 
0.3%
o 7
 
0.3%
E 2
 
0.1%
x 2
 
0.1%

SaleType
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
WD
1267 
New
 
122
COD
 
43
ConLD
 
9
ConLI
 
5
Other values (4)
 
14

Length

Max length5
Median length2
Mean length2.1582192
Min length2

Characters and Unicode

Total characters3151
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1267
86.8%
New 122
 
8.4%
COD 43
 
2.9%
ConLD 9
 
0.6%
ConLI 5
 
0.3%
ConLw 5
 
0.3%
CWD 4
 
0.3%
Oth 3
 
0.2%
Con 2
 
0.1%

Length

2023-10-12T00:17:29.461768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.514012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
wd 1267
86.8%
new 122
 
8.4%
cod 43
 
2.9%
conld 9
 
0.6%
conli 5
 
0.3%
conlw 5
 
0.3%
cwd 4
 
0.3%
oth 3
 
0.2%
con 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2854
90.6%
Lowercase Letter 297
 
9.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 1323
46.4%
W 1271
44.5%
N 122
 
4.3%
C 68
 
2.4%
O 46
 
1.6%
L 19
 
0.7%
I 5
 
0.2%
Lowercase Letter
ValueCountFrequency (%)
w 127
42.8%
e 122
41.1%
o 21
 
7.1%
n 21
 
7.1%
t 3
 
1.0%
h 3
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3151
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 1323
42.0%
W 1271
40.3%
w 127
 
4.0%
N 122
 
3.9%
e 122
 
3.9%
C 68
 
2.2%
O 46
 
1.5%
o 21
 
0.7%
n 21
 
0.7%
L 19
 
0.6%
Other values (3) 11
 
0.3%

SaleCondition
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Normal
1198 
Partial
125 
Abnorml
 
101
Family
 
20
Alloca
 
12

Length

Max length7
Median length6
Mean length6.1575342
Min length6

Characters and Unicode

Total characters8990
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1198
82.1%
Partial 125
 
8.6%
Abnorml 101
 
6.9%
Family 20
 
1.4%
Alloca 12
 
0.8%
AdjLand 4
 
0.3%

Length

2023-10-12T00:17:29.569177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-12T00:17:29.615751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 1198
82.1%
partial 125
 
8.6%
abnorml 101
 
6.9%
family 20
 
1.4%
alloca 12
 
0.8%
adjland 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7526
83.7%
Uppercase Letter 1464
 
16.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1484
19.7%
l 1468
19.5%
r 1424
18.9%
m 1319
17.5%
o 1311
17.4%
i 145
 
1.9%
t 125
 
1.7%
n 105
 
1.4%
b 101
 
1.3%
y 20
 
0.3%
Other values (3) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
N 1198
81.8%
P 125
 
8.5%
A 117
 
8.0%
F 20
 
1.4%
L 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 8990
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1484
16.5%
l 1468
16.3%
r 1424
15.8%
m 1319
14.7%
o 1311
14.6%
N 1198
13.3%
i 145
 
1.6%
P 125
 
1.4%
t 125
 
1.4%
A 117
 
1.3%
Other values (8) 274
 
3.0%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-10-12T00:17:29.670770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2023-10-12T00:17:29.732215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Interactions

2023-10-12T00:17:26.589461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.300247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.686318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.082752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.476575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.894234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.280784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.648686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.193523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.631574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.343475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.724296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.124118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.517368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.935303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.318359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.690226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.235022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.676234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.382956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.770207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.165184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.558296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.976642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.357551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.731804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.274614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.720501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.427463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.823416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.208620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.603505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.022990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.398911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.925270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.319203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.765696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.471099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.871236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.252947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.648646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.066664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.441975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.971491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.364408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.827135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.512774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.916915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.297754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.711411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.107571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.482357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.016154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.409492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.869412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.551774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.954705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.338835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.755019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.147695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.520050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.057755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.454404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.917267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.596242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.999201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.385772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.803769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.193275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.563782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.102275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.500643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.960948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:23.637898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.039849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.429723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:24.847771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.235683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:25.605243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.147261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-10-12T00:17:26.542827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-10-12T00:17:29.783004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
LotAreaOverallQualYearBuiltYearRemodAddTotalBsmtSFGrLivAreaBedroomAbvGrTotRmsAbvGrdSalePriceMSZoningHouseStyleExterQualFoundationBsmtQualCentralAirElectricalFullBathKitchenAbvGrFireplacesGarageCarsGarageCondSaleTypeSaleCondition
LotArea1.0000.2330.1030.0750.3660.4490.3380.4060.4560.0000.0000.0000.0000.0000.0000.0000.0980.0000.1600.0110.0000.0000.000
OverallQual0.2331.0000.6470.5580.4600.6030.1220.4280.8100.1900.1440.6140.2910.5100.3740.1600.4040.1060.2670.4020.1090.1620.152
YearBuilt0.1030.6471.0000.6840.4270.288-0.0350.1770.6530.2950.2910.4350.5020.5160.4380.1880.3510.2140.1690.3400.2010.1570.198
YearRemodAdd0.0750.5580.6841.0000.2990.282-0.0540.1980.5710.2020.2000.3890.3220.3920.3780.2210.2700.1140.1360.2760.1110.2070.259
TotalBsmtSF0.3660.4600.4270.2991.0000.3710.0590.2340.6030.1190.1640.3190.2330.2930.2230.0640.2360.0700.3200.2600.0000.1080.131
GrLivArea0.4490.6030.2880.2820.3711.0000.5430.8280.7310.1060.2580.2860.1520.2490.1580.0080.4680.0000.3760.2870.0560.0350.084
BedroomAbvGr0.3380.122-0.035-0.0540.0590.5431.0000.6680.2350.1650.2420.1710.0860.0900.1600.0810.4480.2330.1070.1340.0270.0570.105
TotRmsAbvGrd0.4060.4280.1770.1980.2340.8280.6681.0000.5330.1750.2660.2740.1180.1890.1120.0790.3890.1740.2230.2420.0000.0470.086
SalePrice0.4560.8100.6530.5710.6030.7310.2350.5331.0000.2060.1290.4760.2580.4550.4180.1350.4160.0460.2890.4160.1050.1280.168
MSZoning0.0000.1900.2950.2020.1190.1060.1650.1750.2061.0000.1840.2390.2240.1910.2970.1030.1750.0910.1360.1440.0740.1510.136
HouseStyle0.0000.1440.2910.2000.1640.2580.2420.2660.1290.1841.0000.1760.2160.2120.2330.1090.2360.1500.0990.1640.1400.0550.086
ExterQual0.0000.6140.4350.3890.3190.2860.1710.2740.4760.2390.1761.0000.3710.4620.2780.1380.3180.0880.1850.3610.0540.2600.236
Foundation0.0000.2910.5020.3220.2330.1520.0860.1180.2580.2240.2160.3711.0000.4050.3650.1820.2850.1670.1200.2700.1400.1500.158
BsmtQual0.0000.5100.5160.3920.2930.2490.0900.1890.4550.1910.2120.4620.4051.0000.2140.2180.3470.0760.1780.4020.1590.2450.246
CentralAir0.0000.3740.4380.3780.2230.1580.1600.1120.4180.2970.2330.2780.3650.2141.0000.4210.1030.2450.1960.2830.2910.1280.113
Electrical0.0000.1600.1880.2210.0640.0080.0810.0790.1350.1030.1090.1380.1820.2180.4211.0000.1160.1070.0850.1260.2430.0000.159
FullBath0.0980.4040.3510.2700.2360.4680.4480.3890.4160.1750.2360.3180.2850.3470.1030.1161.0000.1130.1800.3290.0590.1320.184
KitchenAbvGr0.0000.1060.2140.1140.0700.0000.2330.1740.0460.0910.1500.0880.1670.0760.2450.1070.1131.0000.0860.1230.2670.0000.322
Fireplaces0.1600.2670.1690.1360.3200.3760.1070.2230.2890.1360.0990.1850.1200.1780.1960.0850.1800.0861.0000.2020.0350.0770.085
GarageCars0.0110.4020.3400.2760.2600.2870.1340.2420.4160.1440.1640.3610.2700.4020.2830.1260.3290.1230.2021.0000.0830.1910.213
GarageCond0.0000.1090.2010.1110.0000.0560.0270.0000.1050.0740.1400.0540.1400.1590.2910.2430.0590.2670.0350.0831.0000.0000.029
SaleType0.0000.1620.1570.2070.1080.0350.0570.0470.1280.1510.0550.2600.1500.2450.1280.0000.1320.0000.0770.1910.0001.0000.471
SaleCondition0.0000.1520.1980.2590.1310.0840.1050.0860.1680.1360.0860.2360.1580.2460.1130.1590.1840.3220.0850.2130.0290.4711.000

Missing values

2023-10-12T00:17:27.035479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-12T00:17:27.160557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-12T00:17:27.238639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

MSZoningLotAreaHouseStyleOverallQualYearBuiltYearRemodAddExterQualFoundationBsmtQualTotalBsmtSFCentralAirElectricalGrLivAreaFullBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsGarageCondSaleTypeSaleConditionSalePrice
0RL84502Story720032003GdPConcGd856YSBrkr1710231802TAWDNormal208500
1RL96001Story619761976TACBlockGd1262YSBrkr1262231612TAWDNormal181500
2RL112502Story720012002GdPConcGd920YSBrkr1786231612TAWDNormal223500
3RL95502Story719151970TABrkTilTA756YSBrkr1717131713TAWDAbnorml140000
4RL142602Story820002000GdPConcGd1145YSBrkr2198241913TAWDNormal250000
5RL141151.5Fin519931995TAWoodGd796YSBrkr1362111502TAWDNormal143000
6RL100841Story820042005GdPConcEx1686YSBrkr1694231712TAWDNormal307000
7RL103822Story719731973TACBlockGd1107YSBrkr2090231722TAWDNormal200000
8RM61201.5Fin719311950TABrkTilTA952YFuseF1774222822TAWDAbnorml129900
9RL74201.5Unf519391950TABrkTilTA991YSBrkr1077122521TAWDNormal118000
MSZoningLotAreaHouseStyleOverallQualYearBuiltYearRemodAddExterQualFoundationBsmtQualTotalBsmtSFCentralAirElectricalGrLivAreaFullBathBedroomAbvGrKitchenAbvGrTotRmsAbvGrdFireplacesGarageCarsGarageCondSaleTypeSaleConditionSalePrice
1450RL90002Story519741974TACBlockGd896YSBrkr1792242800NaNWDNormal136000
1451RL92621Story820082009GdPConcGd1573YSBrkr1578231713TANewPartial287090
1452RM3675SLvl520052005TAPConcGd547YSBrkr1072121502TAWDNormal145000
1453RL172171Story520062006TAPConcGd1140YSBrkr1140131600NaNWDAbnorml84500
1454FV75001Story720042005GdPConcGd1221YSBrkr1221221602TAWDNormal185000
1455RL79172Story619992000TAPConcGd953YSBrkr1647231712TAWDNormal175000
1456RL131751Story619781988TACBlockGd1542YSBrkr2073231722TAWDNormal210000
1457RL90422Story719412006ExStoneTA1152YSBrkr2340241921TAWDNormal266500
1458RL97171Story519501996TACBlockTA1078YFuseA1078121501TAWDNormal142125
1459RL99371Story519651965GdCBlockTA1256YSBrkr1256131601TAWDNormal147500